ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Normalized Ship Fuel-Speed Curve for Monitoring CO₂ Emissions by Multi-Sensor System Data

12 September 2017, 15:10 – 15:30

Abstract

Submitted by
Christian Capezza
Authors
Dario Bocchetti (Grimaldi Group), Antonio Lepore (University of Naples Federico II), Biagio Palumbo (University of Naples Federico II), Christian Capezza (University of Naples Federico II), Fabio Centofanti (University of Naples Federico II)
Abstract
The assessment of CO₂ emissions is crucial in the international maritime shipping sector, as required by air pollution programs. Policy makers shall promote the adoption of cost-effective measures to reduce air emissions that need to be verified by practitioners through precise but easy-to-implement methods. In this regard, marine engineers typically rely on ship fuel-speed curves, in which the fuel consumption per hour is put in relationship only with the ship’s speed over ground. However, the fuel consumption variability is affected by numerous additional factors in real cases, hence these curves result inadequate for decision-making purposes. Nonetheless, multi-sensor systems installed on-board of modern ships collect huge amounts of operational data that have great potential for solving this issue. By means of real operational data automatically acquired from a Ro-Pax cruise ship owned by the Italian shipping company Grimaldi Group, this work proposes a new approach based on the orthogonal least squares-partial least squares (LS-PLS) method that is able to take into account the information from operational data and allows the construction of a normalized curve that can be suitably used to monitor and benchmark CO₂ emissions. Furthermore, fault diagnosis can be supported by the implementation of a modified Hotelling’s statistic for scores and a squared prediction error. The approach advantage results in the curve ability to graphically show anomalies, to provide hypothesis tests on the effectiveness of energy efficiency initiatives, and to estimate CO₂ emission reduction that allows stakeholders to claim for carbon credits.

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